Research on Residual Life Prediction of Electrolytic Cells Based on Informer

The lifespan of aluminum electrolytic cells directly affects the economic benefits of electrolytic aluminum enterprises. By predicting the remaining lifespan of aluminum electrolytic cells, maintenance plans can be formulated in advance, thereby reducing economic losses. In this paper, a large number of data generated in the production process of aluminum reduction cells are processed with outlier, missing values, normalization, etc., and the data are dimensionally reduced using Kernel principal component analysis. The remaining service life of aluminum reduction cells is predicted using Informer model. The experimental results show that the Informer model has higher prediction accuracy than the Long short-term memory network and time convolution network, and can more accurately predict the remaining life of aluminum reduction cells, which has an important guiding significance for the management of aluminum reduction cells.